Manufacturing and Industrial Engineering Expert

You will create and assess difficult technical problems used to evaluate AI systems. The work turns real manufacturing and industrial engineering judgment into structured problems with realistic constraints, supporting data, correct analyses, and clear review standards.

• Design challenging problems in manufacturing process design, production systems, quality engineering, automation, supply chain, or operations modeling.
• Provide correct solutions and explain your reasoning clearly enough for another engineer to verify.
• Define objective criteria that help reviewers distinguish sound solutions from plausible but incorrect ones.
• Review problems created by other contributors for technical accuracy and sound methods.
• Identify problems that are ambiguous or cannot be graded reliably.

What it pays and takes

This is flexible, fully remote contractor work that can fit alongside your current role. The structured experience level is entry level, and the role is part time.

• Pay: $85 to $185 per hour.
• Work arrangement: Fully remote, part time, and flexible.
• Contract type: Contractor.
• Location: Open to candidates in the listed countries across the Americas, Europe, Asia, Africa, and the Middle East.
• Language: Professional fluency in English.
• Education: A master's degree or PhD in manufacturing, industrial, mechanical engineering, or a closely related field.
• Experience: Principal, staff, or fellow-level experience at a major manufacturing organization, or a faculty appointment at a leading university.
• Technical background: Deep expertise in at least one relevant subdomain and hands-on ownership of engineering work.
• Communication: Strong technical communication and sound judgment when interpreting constraints.
• Helpful background: Publications, patents, technical leadership, or experience with AI or LLM evaluation.

How it works

Apply on OpenTrain with your resume, then complete the application on the hiring site.

About AI training work

AI training and data-labeling work is the human side of building artificial intelligence. Experts create and review examples, problems, and model responses so AI systems can produce more accurate and useful results.